NDLI: Wavelet SVM ensemble for pattern classification with quantum-inspired evolutionary algorithm

Content Provider	IEEE Xplore Digital Library
Author	Zhi-Yong Luo Wen-Feng Zhang Bin-Yuan Ye Lin-Qin Cai
Copyright Year	2008
Description	Author affiliation: Coll. of Autom., Chongqing Univ. of Posts & Telecommun., Chongqing (Zhi-Yong Luo)
Abstract	Based on quantum-inspired evolutionary algorithm (QEA), a novel approach of constructing multi-class least squares wavelet SVM (LS-WSVM) ensemble classifiers is presented, regularization parameters and kernel parameters of LS-WSVM can be optimized. Quantum-inspired evolutionary optimization can get appropriate parameters of LS-WSVM with global search, so the LS-WSVM ensemble model with boosting for the multi-class classifiers is built. And then, classification is studied using single base LS-SVM and LS-SVM ensemble with wavelet and Gaussian kernel, respectively. The simulation results show that the approach for the multi-class LS-WSVM ensemble classifiers is effective, that can obtain the optimal parameters of LS-WSVM with global searching QEA, and improved LS-WSVM provides excellent precision for ensemble classification.
Starting Page	485
Ending Page	490
File Size	250870
Page Count	6
File Format	PDF
ISBN	9781424422388
DOI	10.1109/ICWAPR.2008.4635829
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-30
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Evolutionary computation Boosting Classification algorithms Parameter optimization Kernel LS-WSVM ensemble classifiers Least squares wavelet support vector machine (LS-WSVM) Optimization Biological cells Quantum-inspired evolutionary algorithm (QEA)
Content Type	Text
Resource Type	Article

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